OpenRaiser/Intern-Atlas huggingface dataset:
Beyond the Min-Cut Bound: Deterministic Network Coding for Asynchronous Multirate Broadcast
Venue: arXiv cs.IT 2014
Year: 2014

In a single hop broadcast packet erasure network, we demonstrate that it is possible to provide multirate packet delivery outside of what is given by the network min-cut. This is achieved by using a deterministic non-block-based network coding scheme, which allows us to sidestep some of the limitations put in place by the block coding model used to determine the network capacity. Under the network coding scheme we outline, the sender is able to transmit network coded packets above the channel rate of some receivers, while ensuring that they still experience nonzero delivery rates. Interestingly, in this generalised form of asynchronous network coded broadcast, receivers are not required to obtain knowledge of all packets transmitted so far. Instead, causal feedback from the receivers about packet erasures is used by the sender to determine a network coded transmission that will allow at least one, but often multiple receivers, to deliver their next needed packet. Although the analysis of deterministic coding schemes is generally a difficult problem, by making some approximations we are able to obtain tractable estimates of the receivers' delivery rates, which are shown to match reasonably well with simulation. Using these estimates, we design a fairness algorithm that allocates the sender's resources so all receivers will experience fair delivery rate performance.
Adaptive-Rate Compressive Sensing Using Side Information
Venue: arXiv cs.CV 2014
Year: 2014

We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. Our first method utilizes extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprise the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. For each image in the video sequence, our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences.
Uncertainty-based information extraction in wireless sensor networks for control applications
Venue: arXiv cs.NI 2014
Year: 2014

Design of control applications over wireless sensor networks (WSNs) is a challenging issue due to the bandwidth-limited communication medium, energy constraints and real-time data delivery requirements. This paper introduces a new information extraction method for WSN-based control applications, which reduces the number of required data transmissions to save energy and avoid data congestion. According to the proposed approach, control applications recognize when new data readings have to be collected and determine sensor nodes that have to be activated on the basis of uncertainty analysis. Processing of the selectively collected input data is based on definition of information granules that describe state of the controlled system as well as performance of particular control decisions. This method was implemented for object tracking in WSNs. The task is to control movement of a mobile sink, which has to reach a target in the shortest possible time. Extensive simulation experiments were performed to compare performance of the proposed approach against state-of-the-art methods. Results of the experiments show that the presented information extraction method allows for substantial reduction in the amount of transmitted data with no significant negative effect on tracking performance.
Computing Entropy Rate Of Symbol Sources & A Distribution-free Limit Theorem
Venue: arXiv cs.IT 2014
Year: 2014

Entropy rate of sequential data-streams naturally quantifies the complexity of the generative process. Thus entropy rate fluctuations could be used as a tool to recognize dynamical perturbations in signal sources, and could potentially be carried out without explicit background noise characterization. However, state of the art algorithms to estimate the entropy rate have markedly slow convergence; making such entropic approaches non-viable in practice. We present here a fundamentally new approach to estimate entropy rates, which is demonstrated to converge significantly faster in terms of input data lengths, and is shown to be effective in diverse applications ranging from the estimation of the entropy rate of English texts to the estimation of complexity of chaotic dynamical systems. Additionally, the convergence rate of entropy estimates do not follow from any standard limit theorem, and reported algorithms fail to provide any confidence bounds on the computed values. Exploiting a connection to the theory of probabilistic automata, we establish a convergence rate of $O(\log \vert s \vert/\sqrt[3]{\vert s \vert})$ as a function of the input length $\vert s \vert$, which then yields explicit uncertainty estimates, as well as required data lengths to satisfy pre-specified confidence bounds.
What is usual in unusual videos? Trajectory snippet histograms for discovering unusualness
Venue: arXiv cs.CV 2014
Year: 2014

Unusual events are important as being possible indicators of undesired consequences. Moreover, unusualness in everyday life activities may also be amusing to watch as proven by the popularity of such videos shared in social media. Discovery of unusual events in videos is generally attacked as a problem of finding usual patterns, and then separating the ones that do not resemble to those. In this study, we address the problem from the other side, and try to answer what type of patterns are shared among unusual videos that make them resemble to each other regardless of the ongoing event. With this challenging problem at hand, we propose a novel descriptor to encode the rapid motions in videos utilizing densely extracted trajectories. The proposed descriptor, which is referred to as trajectory snipped histograms, is used to distinguish unusual videos from usual videos, and further exploited to discover snapshots in which unusualness happen. Experiments on domain specific people falling videos and unrestricted funny videos show the effectiveness of our method in capturing unusualness.
Predicting a Business Star in Yelp from Its Reviews Text Alone
Venue: arXiv cs.IR 2014
Year: 2014

Yelp online reviews are invaluable source of information for users to choose where to visit or what to eat among numerous available options. But due to overwhelming number of reviews, it is almost impossible for users to go through all reviews and find the information they are looking for. To provide a business overview, one solution is to give the business a 1-5 star(s). This rating can be subjective and biased toward users personality. In this paper, we predict a business rating based on user-generated reviews texts alone. This not only provides an overview of plentiful long review texts but also cancels out subjectivity. Selecting the restaurant category from Yelp Dataset Challenge, we use a combination of three feature generation methods as well as four machine learning models to find the best prediction result. Our approach is to create bag of words from the top frequent words in all raw text reviews, or top frequent words/adjectives from results of Part-of-Speech analysis. Our results show Root Mean Square Error (RMSE) of 0.6 for the combination of Linear Regression with either of the top frequent words from raw data or top frequent adjectives after Part-of-Speech (POS).
Feature Selection Using Classifier in High Dimensional Data
Venue: arXiv cs.CV 2014
Year: 2014

Feature selection is frequently used as a pre-processing step to machine learning. It is a process of choosing a subset of original features so that the feature space is optimally reduced according to a certain evaluation criterion. The central objective of this paper is to reduce the dimension of the data by finding a small set of important features which can give good classification performance. We have applied filter and wrapper approach with different classifiers QDA and LDA respectively. A widely-used filter method is used for bioinformatics data i.e. a univariate criterion separately on each feature, assuming that there is no interaction between features and then applied Sequential Feature Selection method. Experimental results show that filter approach gives better performance in respect of Misclassification Error Rate.
A Search Procedure for Cyclic Subsets
Venue: arXiv cs.DS 2014
Year: 2014

In this paper, a polynomial time algorithm for finding the set of all cyclic subsets in a graph is presented. The concept of cyclic subsets has already been introduced in an earlier paper. The algorithm finds cyclic subsets in a graph G by conjoining building block subsets of length three in V(G). We prove the correctness of this algorithm and present an asymptotic time complexity analysis of the algorithm's performance.
LB2CO: A Semantic Ontology Framework for B2C eCommerce Transaction on the Internet
Venue: arXiv cs.CY 2014
Year: 2014

Business ontology can enhance the successful development of complex enterprise system; this is being achieved through knowledge sharing and the ease of communication between every entity in the domain. Through human semantic interaction with the web resources, machines to interpret the data published in a machine interpretable form under web. However, the theoretical practice of business ontology in eCommerce domain is quite a few especially in the section of electronic transaction, and the various techniques used to obtain efficient communication across spheres are error prone and are not always guaranteed to be efficient in obtaining desired result due to poor semantic integration between entities. To overcome the poor semantic integration this research focuses on proposed ontology called LB2CO, which combines the framework of IDEF5 & SNAP as an analysis tool, for automated recommendation of product and services and create effective ontological framework for B2C transaction & communication across different business domains that facilitates the interoperability & integration of B2C transactions over the web.
On Verifying Resource Contracts using Code Contracts
Venue: arXiv cs.SE 2014
Year: 2014

In this paper we present an approach to check resource consumption contracts using an off-the-shelf static analyzer. We propose a set of annotations to support resource usage specifications, in particular, dynamic memory consumption constraints. Since dynamic memory may be recycled by a memory manager, the consumption of this resource is not monotone. The specification language can express both memory consumption and lifetime properties in a modular fashion. We develop a proof-of-concept implementation by extending Code Contracts' specification language. To verify the correctness of these annotations we rely on the Code Contracts static verifier and a points-to analysis. We also briefly discuss possible extensions of our approach to deal with non-linear expressions.
BEval: A Plug-in to Extend Atelier B with Current Verification Technologies
Venue: arXiv cs.SE 2014
Year: 2014

This paper presents BEval, an extension of Atelier B to improve automation in the verification activities in the B method or Event-B. It combines a tool for managing and verifying software projects (Atelier B) and a model checker/animator (ProB) so that the verification conditions generated in the former are evaluated with the latter. In our experiments, the two main verification strategies (manual and automatic) showed significant improvement as ProB's evaluator proves complementary to Atelier B built-in provers. We conducted experiments with the B model of a micro-controller instruction set; several verification conditions, that we were not able to discharge automatically or manually with AtelierB's provers, were automatically verified using BEval.
Analyzing Behavioural Scenarios over Tabular Specifications Using Model Checking
Venue: arXiv cs.SE 2014
Year: 2014

Tabular notations, in particular SCR specifications, have proved to be a useful means for formally describing complex requirements. The SCR method offers a powerful family of analysis tools, known as the SCR Toolset, but its availability is restricted by the Naval Research Laboratory of the USA. This toolset applies different kinds of analysis considering the whole set of behaviours associated with a requirements specification. In this paper we present a tool for describing and analyzing SCR requirements descriptions, that complements the SCR Toolset in two aspects. First, its use is not limited by any institution, and resorts to a standard model checking tool for analysis; and second, it allows to concentrate the analysis to particular sets of behaviours (subsets of the whole specifications), that correspond to particular scenarios explicitly mentioned in the specification. We take an operational notation that allows the engineer to describe behavioural "scenarios" by means of programs, and provide a translation into Promela to perform the analysis via Spin, an efficient off-the-shelf model checker freely available. In addition, we apply the SCR method to a Pacemaker system and we use its tabular specification as a running example of this article.
Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output
Venue: arXiv cs.DB 2014
Year: 2014

We consider accurately answering smooth queries while preserving differential privacy. A query is said to be $K$-smooth if it is specified by a function defined on $[-1,1]^d$ whose partial derivatives up to order $K$ are all bounded. We develop an $ε$-differentially private mechanism for the class of $K$-smooth queries. The major advantage of the algorithm is that it outputs a synthetic database. In real applications, a synthetic database output is appealing. Our mechanism achieves an accuracy of $O (n^{-\frac{K}{2d+K}}/ε)$, and runs in polynomial time. We also generalize the mechanism to preserve $(ε, δ)$-differential privacy with slightly improved accuracy. Extensive experiments on benchmark datasets demonstrate that the mechanisms have good accuracy and are efficient.
On the likelihood of multiple bit upsets in logic circuits
Venue: arXiv cs.AR 2014
Year: 2014

Soft errors have a significant impact on the circuit reliability at nanoscale technologies. At the architectural level, soft errors are commonly modeled by a probabilistic bit-flip model. In developing such abstract fault models, an important issue to consider is the likelihood of multiple bit errors caused by particle strikes. This likelihood has been studied to a great extent in memories, but has not been understood to the same extent in logic circuits. In this paper, we attempt to quantify the likelihood that a single transient event can cause multiple bit errors in logic circuits consisting of combinational gates and flip-flops. In particular, we calculate the conditional probability of multiple bit-flips given that a single bit flips as a result of the transient. To calculate this conditional probability, we use a Monte Carlo technique in which samples are generated using detailed post-layout circuit simulations. Our experiments on the ISCAS'85 benchmarks and a few other circuits indicate that, this conditional probability is quite significant and can be as high as 0.31. Thus we conclude that multiple bit-flips must necessarily be considered in order to obtain a realistic architectural fault model for soft errors.
Solver Scheduling via Answer Set Programming
Venue: arXiv cs.AI 2014
Year: 2014

Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 SAT Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, non-uniform and ordered solver schedules from existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.
Constraint Solvers for User Interface Layout
Venue: arXiv cs.HC 2014
Year: 2014

Constraints have played an important role in the construction of GUIs, where they are mainly used to define the layout of the widgets. Resizing behavior is very important in GUIs because areas have domain specific parameters such as form the resizing of windows. If linear objective function is used and window is resized then error is not distributed equally. To distribute the error equally, a quadratic objective function is introduced. Different algorithms are widely used for solving linear constraints and quadratic problems in a variety of different scientific areas. The linear relxation, Kaczmarz, direct and linear programming methods are common methods for solving linear constraints for GUI layout. The interior point and active set methods are most commonly used techniques to solve quadratic programming problems. Current constraint solvers designed for GUI layout do not use interior point methods for solving a quadratic objective function subject to linear equality and inequality constraints. In this paper, performance aspects and the convergence speed of interior point and active set methods are compared along with one most commonly used linear programming method when they are implemented for graphical user interface layout. The performance and convergence of the proposed algorithms are evaluated empirically using randomly generated UI layout specifications of various sizes. The results show that the interior point algorithms perform significantly better than the Simplex method and QOCA-solver, which uses the active set method implementation for solving quadratic optimization.
Distribution-Sensitive Construction of the Greedy Spanner
Venue: arXiv cs.CG 2014
Year: 2014

The greedy spanner is the highest quality geometric spanner (in e.g. edge count and weight, both in theory and practice) known to be computable in polynomial time. Unfortunately, all known algorithms for computing it take Omega(n^2) time, limiting its applicability on large data sets. We observe that for many point sets, the greedy spanner has many `short' edges that can be determined locally and usually quickly, and few or no `long' edges that can usually be determined quickly using local information and the well-separated pair decomposition. We give experimental results showing large to massive performance increases over the state-of-the-art on nearly all tests and real-life data sets. On the theoretical side we prove a near-linear expected time bound on uniform point sets and a near-quadratic worst-case bound. Our bound for point sets drawn uniformly and independently at random in a square follows from a local characterization of t-spanners we give on such point sets: we give a geometric property that holds with high probability on such point sets. This property implies that if an edge set on these points has t-paths between pairs of points `close' to each other, then it has t-paths between all pairs of points. This characterization gives a O(n log^2 n log^2 log n) expected time bound on our greedy spanner algorithm, making it the first subquadratic time algorithm for this problem on any interesting class of points. We also use this characterization to give a O((n + |E|) log^2 n log log n) expected time algorithm on uniformly distributed points that determines if E is a t-spanner, making it the first subquadratic time algorithm for this problem that does not make assumptions on E.
Power Grid Defense Against Malicious Cascading Failure
Venue: arXiv cs.CR 2014
Year: 2014

An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has the capability to launch cyber-attacks as practical concerns (i.e. avoiding disruption of service, presence of legacy systems, etc.) may hinder security. Hence, a defender can harden the security posture at certain power stations but may lack the time and resources to do this for the entire power grid. We model a power grid as a graph and introduce the cascading failure game in which both the defender and attacker choose a subset of power stations such as to minimize (maximize) the number of consumers having access to producers of power. We formalize problems for identifying both mixed and deterministic strategies for both players, prove complexity results under a variety of different scenarios, identify tractable cases, and develop algorithms for these problems. We also perform an experimental evaluation of the model and game on a real-world power grid network. Empirically, we noted that the game favors the attacker as he benefits more from increased resources than the defender. Further, the minimax defense produces roughly the same expected payoff as an easy-to-compute deterministic load based (DLB) defense when played against a minimax attack strategy. However, DLB performs more poorly than minimax defense when faced with the attacker's best response to DLB. This is likely due to the presence of low-load yet high-payoff nodes, which we also found in our empirical analysis.
Structured random measurements in signal processing
Venue: arXiv cs.IT 2014
Year: 2014

Compressed sensing and its extensions have recently triggered interest in randomized signal acquisition. A key finding is that random measurements provide sparse signal reconstruction guarantees for efficient and stable algorithms with a minimal number of samples. While this was first shown for (unstructured) Gaussian random measurement matrices, applications require certain structure of the measurements leading to structured random measurement matrices. Near optimal recovery guarantees for such structured measurements have been developed over the past years in a variety of contexts. This article surveys the theory in three scenarios: compressed sensing (sparse recovery), low rank matrix recovery, and phaseless estimation. The random measurement matrices to be considered include random partial Fourier matrices, partial random circulant matrices (subsampled convolutions), matrix completion, and phase estimation from magnitudes of Fourier type measurements. The article concludes with a brief discussion of the mathematical techniques for the analysis of such structured random measurements.